A number inside a model learned during training and used to make outputs.
Parameters are the tiny knobs inside an AI. Training keeps turning them until it stops sounding like a confused parrot.
More parameters do not mean the AI is always right. They often mean more room to learn. They also mean a scarier bill.
Neural-network
Parameter is an internal value a Neural-network learns through training.
Pretraining
More parameters often need larger Pretraining to work well.
Foundation-model
A Foundation-model's limit often depends on its parameter count.
Scaling-law
Scaling-law describes how more parameters can improve performance.